Short Term Wind Power Prediction Based On Calman Filter Algorithm

As we know the largest problem in the wind power is the wind power volatility. Random variation and instability of the wind speed determines the intermittent of wind power. Wind power does not have the high stability to thermal power, which greatly affect the utilization of wind power. So how to forecast wind power better and obtain more accurate data, which is very important for the use of wind power.The main content of this paper is the prediction of wind power which is based on the Kalman filtering principle. In order to solve the short-term wind power prediction accuracy is not high,and meteorological factors affecting the prediction error and other issues. A new short-term wind power prediction method was put forward in this paper. Firstly, based on the state space model and Kalman filter theory, according to the linear minimum mean variance estimation criterion is derived to achieve the Kalman filter algorithm. Then, based on the analysis of Kalman filter algorithm,according to the algorithm flow and wind power prediction error accuracy, the mathematical model of short-term wind power prediction was set up. Obviously, the model taking into the precise NWP to the improvement of short-term wind power prediction accuracy on the one hand, on the other hand it also takes into the short-term wind power prediction accuracy requirements, so the short-term wind power prediction model proposed in this paper has a high reliability and accuracy. Then, using MATLAB, Software programming achieve short-term wind power prediction method based on Kalman Filter. Finally, as an example of a wind farm on the model verification, the results showed that the use of the Kalman filter method to predict wind power can significantly improve the prediction accuracy, promoting the development of wind power prediction has some theoretical and practical value.